Towards a guideline for evaluation metrics in medical image segmentation

Müller, Dominik; Soto-Rey, Iñaki

doi:10.1186/s13104-022-06096-y

Cited by 203 publications

(95 citation statements)

References 41 publications

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“…This equation is based on the following sub-metrics: IoU, recall, and precision. According to [ 56 ], these metrics are well suited for medical image segmentation, along with the F1 score or Dice similarity coefficient, which is slightly different from IoU because this one penalizes under- and over-segmentation more than the Dice does. The Dice coefficient is often used to quantify the performance of image segmentation methods.…”

Section: Evaluations and Resultsmentioning

confidence: 99%

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

et al. 2022

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Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder–Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

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Section: Evaluations and Resultsmentioning

confidence: 99%

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

et al. 2022

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“…The results are obtained using the developed deep learning architecture on the PH2 database images [24,25] 5 and Table 1 shows the performance metrics used to show the effectiveness of the proposed system. The commonly used performance metrics [26,27], such as accuracy, sensitivity and specificity are employed in this study. To attain better performance, the proposed DermICNet for SCD requires more images, so data augmentation [28][29][30] is employed.…”

Section: Resultsmentioning

confidence: 99%

DermICNet: Efficient Dermoscopic Image Classification Network for Automated Skin Cancer Diagnosis

Manikandan¹,

Karthikeyan²

2022

RIA

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The incidence of skin cancer is rapidly increasing worldwide. The relevance of Skin Cancer Diagnosis (SCD) and the difficulty in achieving an accurate and consistent diagnosis have resulted in significant research interest. Furthermore, automated detection or classification would be even more helpful in a diagnostic assistance system. This study develops an efficient Dermoscopic Image Classification Network (DermICNet) for automated SCD. The proposed DermICNet is a deep learning architecture with an efficient arrangement of eight convolutional layers with small-sized convolution filters (3x3). The extracted features from the convolution layers are fed to the dense layer for classification. It consists of a neural network that uses stochastic gradient descent optimization to find the optimal solution for SCD. Finally, a softmax classifier is employed to classify the patterns in the dermoscopic images. The proposed DermICNet is assessed using PH2 database images. The classification results reported are based on the random-split (70:30) approach, which divides the PH2 database into training and testing. It is demonstrated that it is feasible to discriminate between abnormal and normal dermoscopic images with an average accuracy of 99.2% using the proposed DermICNet. The results suggest that the analysis of dermoscopic images using DermICNet has the potential as a diagnostic tool for SCD.

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“…The trained model generates segmented binary masks for the test images, which are then compared to the ground truth binary mask for the test images. The proposed model’s performance is evaluated and compared to that of other segmentation models using the most commonly used evaluation matrices for semantic segmentation, including the dice similarity coefficient (DSC), intersection over union (IoU), accuracy, recall, precision, and specificity [ 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network

Shoaib

Chuah

Raza

et al. 2023

Life

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The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, the well-known state-of-the-art segmentation models still lack in terms of accuracy and speed. This study aims to develop a single-stage lightweight segmentation model that precisely and rapidly segments the LV from 2D echocardiography images. In this research, a backbone network is used to acquire both low-level and high-level features. Two parallel blocks, known as the spatial feature unit and the channel feature unit, are employed for the enhancement and improvement of these features. The refined features are merged by an integrated unit to segment the LV. The performance of the model and the time taken to segment the LV are compared to other established segmentation models, DeepLab, FCN, and Mask RCNN. The model achieved the highest values of the dice similarity index (0.9446), intersection over union (0.8445), and accuracy (0.9742). The evaluation metrics and processing time demonstrate that the proposed model not only provides superior quantitative results but also trains and segments the LV in less time, indicating its improved performance over competing segmentation models.

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Towards a guideline for evaluation metrics in medical image segmentation

Cited by 203 publications

References 41 publications

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

DermICNet: Efficient Dermoscopic Image Classification Network for Automated Skin Cancer Diagnosis

Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network

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